This program is for forced classification of dual scaling.
Usage
dsFC(X, Crit, dim)
Arguments
X
The Initial Data.
Crit
The criterion item for forced classification.
dim
The maximun number of components to be extracted.
Value
Match
Match-mismatch tables
Predict
Correct prediction percentages
Proj.Op_A
Projected options weights
Proj.Su_A
Projected subject scores
Inf_A
Distribution of information over components
ItemStat_A
Item statistics
Out_A
Results obtained by forced classification
Rij_A
Inter-item correlation
Norm.Op_A
Normed options weights
Norm.Su_A
Normed subject scores
Details
There are three types of outputs: Forced classification of the criterion item (type A); dual scaling of
non-criterion items by ignoring the criterion item (type B); dual scaling of non-criterion items after eliminating
the influence of the criterion item (type C). These three types correspond to, respectively, dual scaling of data projected onto the subspace of the criterion item, dual scaling of non-criterion items, and dual scaling of
data in the complementary space of the criterion item.
References
Nishisato (1984). Forced classification: A simple application of a quantification technique.
Psychometrika, 49, 25-36.